For some time it has been possible to control machines through the use of remote signaling devices in which infrared or RF signals are used to control machine function. From VCRs, TVs, to stereo equipment and computers, battery-powered hand-held devices eliminate the requirement for the individual to move to the machine to activate machine-carried buttons or switches as well as wired control modules.
However, as in the case of television, hand-held remote control modules which control the operation of the television in terms of channel, volume and in some cases hue, density and contrast are easily lost or misplaced. Even if not lost, they may be in another location, requiring the viewers to retrieve them. As a result there can be considerable frustration on the part of television viewers when they wish to turn on or control the television. Moreover, with respect to computer workstations, control of the workstation with a hand-held remote control unit also suffers the same problems of loss of the remote control unit.
Additionally, and perhaps more importantly, providing a wide variety of buttons on a remote control module is often confusing to the user because of the multiple buttons. The large number of buttons was necessitated by the large number of functions to be controlled. The more functions, the more buttons which adds to the complexity of activating the various functions.
As a result there is a requirement for remoteless TV control which is instantly learnable in terms of instructing an individual as to how to operate the TV. The remoteless TV control must be easy to remember, easy to master, and reliable. Moreover, the penalty for false entry into a control mode must be made relatively small.
By way of background, the following articles describe machine control and the use of pattern and orientation analysis in the detection of objects within an optically scanned scene: D. H. Ballard and C. M. Brown, editors. Computer Vision. Prentice Hall, 1982; A. Blake and M. Isard. 3D position, attitude and shape input using video tracking of hands and lips. In Proceedings of SIGGRAPH 94, pages 185-192, 1994. In Computer Graphics, Annual Conference Series; T. J. Darrell and A. P. Pentland. Space-time gestures. In Proc. IEEE CVPR, pages 335-340, 1993; J. Davis and M. Shah. Gesture recognition. Technical Report CS-TR-93-11, University of Central Florida, Orlando, Fla. 32816, 1993; W. T. Freeman and E. H. Adelson. The design and use of steerable filters. IEEE Pat. Anal. Mach. Intell., 13(9):891-906, September 1991; M. Kass and A. P. Witkin. Analyzing oriented patterns. In Proc. Ninth IJCAI, pages 944-952, Los Angeles, Calif., August 1985; H. Knutsson and G. H. Granlund. Texture analysis using two-dimensional quadrature filters. In IEEE Computer Society Workshop on Computer Architecture for Pattern Analysis and Image Database Management, pages 206-213, 1983; J. M. Rehg and T. Kanade. Digiteyes: vision-based human hand tracking. Technical Report CMU-CS-93-220, Carnegie Mellon School of Computer Science, Pittsburgh, Pa. 15213, 1983; and J. Segen. Gest: a learning computer vision system that recognizes gestures. In Machine Learning IV. Morgan Kauffman, 1992. edited by Michalski et. al.